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Improved accuracy of screening tools for fetal alcohol spectrum disorder may lead to faster diagnosis

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A new screening instrument has the potential to more accurately identify fetal alcohol spectrum disorder (FASD), reducing missed and erroneous diagnoses in affected children and facilitating treatment and support, a new study suggests. Prenatal alcohol exposure (PAE) is a known cause of birth and growth defects and neurobehavioral issues.

Children with more obvious physical differences may be appropriately diagnosed with (FAS). The majority of affected by PAE, however, present largely with neurobehavioral issues that align with the for (FASD). This condition affects up to 5% of in the US, but they are at high risk of being misdiagnosed—for example, with ADHD—or not diagnosed at all.

This impedes these children's access to tailored interventions, potentially leading to worse outcomes. While researchers have made progress in identifying FASD, a lack of specialist diagnostic training among clinicians highlights an ongoing need for accurate screening methods that are inexpensive and easy to use.

For the study in Alcohol: Clinical & Experimental Research, investigators tested a new diagnostic tool they had developed, the FASD-Tree. This automated web-based screening instrument combines two existing tools, a decision tree, and a risk score assessment. In previous testing, these had yielded positive results (80% and 67–79% diagnostic accuracy, respectively).

Researchers worked with children aged 4–17 recruited from FASD research and clinical programs; 224 had documented PAE. A control group consisted of 78 children without ; some had lower IQ scores or attention problems, which overlap with FASD. The children were examined by expert clinicians for five associated with FASD, in addition to height, weight, and head circumference.

Two parent questionnaires assessed their attention, socialization, rule-breaking, living skills, and more. The FASD-Tree yields a yes/no indicator of FASD and a numeric risk score, which can be considered separately or in combination. The researchers used statistical analysis to evaluate diagnostic results.

Comparing alcohol-exposed children and the , the components of the FASD-Tree classified participants as either PAE or otherwise with 76–84% accuracy. Using the combined measures, accuracy was 81%. The FASD-Tree correctly identified 87% of positive cases (participants exposed to alcohol as fetuses) and 63% of negative cases (those not exposed).

When participants with FASD (meeting criteria for either FAS or alcohol-related neurodevelopmental disorder) were compared to those without FASD (a group that included some alcohol-exposed participants), the components of the FASD-Tree accurately classified them in 75-80% of cases; combining both indicators resulted in 77% accuracy.

Again, the FASD-Tree was more adept at identifying positive cases than negative; this helps captures a broader range of potentially affected children. Overall, its accuracy was fair to good and improves on some clinical tools currently in use. Among children with PAE, children who had PAE were less affected children (with higher IQ scores and fewer attention problems) and were more likely to be Hispanic/Latino.

The FASD-Tree is a valuable and accessible clinical tool with the potential to improve diagnostic accuracy in primary and specialty health care settings. The behavioral and cognitive elements of FASD are its most impairing features; earlier and more appropriate diagnosis, treatment, and services potentially lead to improved outcomes. The researchers caution that the FASD-Tree is a guide to clinical decision-making, not an objective biomarker. Additional testing is needed.

More information: S. Mattson et al, Validation of the FASD-Tree as a screening tool for fetal alcohol spectrum disorders, Alcohol: Clinical & Experimental Research (2023). DOI: 10.1111/acer.14987

Citation: Improved accuracy of screening tools for fetal alcohol spectrum disorder may lead to faster diagnosis (2023, February 20) retrieved 3 June 2023 from
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